Receiving and giving electronic cigarettes as gifts in China: Findings from International Tobacco Control China Survey
Bibliographic record
Abstract
Cigarette gifting is pervasive in China. As the Chinese are increasingly aware of harm from smoking cigarettes, e-cigarettes, often promoted as less harmful alternatives to cigarettes, may be viewed as appropriate gifts. This study is the first using population-based survey data to examine receiving and giving e-cigarettes as gifts in China. We analyzed 9,274 adults from Wave 5 of the International Tobacco Control China Survey, which was completed in July 2015. We found that the prevalence of receiving e-cigarettes as gifts was 1.3% among all respondents and 5.3% among urban smokers; the prevalence of giving e-cigarettes as gifts was 0.5% among all respondents and 1.2% among urban smokers. These prevalence estimates were very low among nonsmokers and rural respondents. Further analysis on urban smokers (N = 3,312) found that those aged 40-54 and 55+, those with high education levels, heavy smokers, and those who perceived e-cigarettes as equally/more harmful than cigarettes were more likely to receive e-cigarette gifts; and those who ever used e-cigarettes were significantly more likely to both receive and give e-cigarette gifts. Urban smokers with positive attitude about cigarette gifting were also more likely to give e-cigarette gifts to others, but those aged 55+ were less likely to gift e-cigarettes. Findings of this study indicate that the Chinese may perceive e-cigarettes as appropriate gifts for smokers, especially heavy smokers. Precautions should be taken to prevent e-cigarettes from becoming a gift choice for nonsmokers. Health campaigns designed to combat the social acceptance of cigarette gifting may also help reduce e-cigarette gifting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".